Storybook MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Storybook as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Storybook. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Storybook?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Storybook MCP Server
Seamlessly integrate your Storybook design system into your conversational AI workflows. Empower front-end engineers and designers to instantly query component libraries, retrieve prop signatures, and extract documentation paths natively within their terminal. By connecting your deployed Storybook instance directly to your AI context, you eliminate context switching, prevent duplicate UI implementations, and accelerate component-driven architecture development across your entire front-end ecosystem.
LlamaIndex agents combine Storybook tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Design System Discovery — Systematically map your component folder structures invoking
list_categoriesand browse all rendered elements across your UI utilizinglist_components. - Component Inspection — Quickly lookup predefined interface elements utilizing
search_componentsto avoid code duplication, and retrieve component properties and metadata viaget_story_args. - Implementation Guidance — Extract local source code paths directly from the Storybook index using
extract_docs_guidanceto efficiently evaluate implementation logic. - Visual Previews — Generate interactive, isolated sandbox iframe endpoints by running
get_preview_urlto safely preview changes before integrating.
The Storybook MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Storybook to LlamaIndex via MCP
Follow these steps to integrate the Storybook MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from Storybook
Why Use LlamaIndex with the Storybook MCP Server
LlamaIndex provides unique advantages when paired with Storybook through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Storybook tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Storybook tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Storybook, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Storybook tools were called, what data was returned, and how it influenced the final answer
Storybook + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Storybook MCP Server delivers measurable value.
Hybrid search: combine Storybook real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Storybook to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Storybook for fresh data
Analytical workflows: chain Storybook queries with LlamaIndex's data connectors to build multi-source analytical reports
Storybook MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Storybook to LlamaIndex via MCP:
extract_docs_guidance
Get guidance on how to read documentation for a component
get_preview_url
Generate the preview URL for a component sandbox
get_story_args
Get metadata and default arguments for a specific component
list_categories
g., Atoms, Molecules, Organisms). List the top-level categories and folder structure of the Design System
list_components
You can optionally filter by category folder. List all UI components available in the Storybook Design System
search_components
Search for specific components by name or keyword
Example Prompts for Storybook in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Storybook immediately.
"Search for Button components in my Storybook and show their props."
"List the categories in the design system and browse the components rendered."
"Extract the local source code paths from the index for the Navigation Bar component and generate an iframe preview."
Troubleshooting Storybook MCP Server with LlamaIndex
Common issues when connecting Storybook to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpStorybook + LlamaIndex FAQ
Common questions about integrating Storybook MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Storybook with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Storybook to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.